Real-time squared: A real-time data set for real-time GDP forecasting

The paper uses real-time data to mimic real-time GDP forecasting activity. Through automatic searches for the best indicators to predict GDP one step and four steps ahead, we compare the out-of-sample forecasting performance of adaptive models using different data vintages and produce three main findings. First, despite data revisions, the forecasting performance of models with indicators is better, but this advantage tends to vanish over longer forecasting horizons. Second, the practice of using fully updated datasets at the time the forecast is made (i.e. taking the best available measures of today’s economic situation) does not appear to bring effective improvement in forecasting ability: the first GDP release is predicted just as well by models using either real-time or the latest available data. Third, although the first release is a rational forecast of GDP data after all statistical revisions have taken place, the forecast based on the latest available GDP data (i.e. the “temporarily best” measures) may be improved by combining preliminary official releases with one-step-ahead forecasts.